Related Experiment Video
Updated: May 13, 2025
08:49
External Cephalic Version: Is it an Effective and Safe Procedure?
Published on: June 6, 2020
14.4K
Calculators for Predicting Risk of Cesarean Delivery: A Literature Review
Shirley J Shao1, Nasim C Sobhani2
1Resident Physician, Department of Obstetrics and Gynecology, Weill Cornell, New York, NY.
Obstetrical & Gynecological Survey
|April 16, 2025
Summary
Obstetric calculators can predict cesarean delivery risk but often lack accuracy in new populations. Clinicians should use these tools cautiously to avoid negatively impacting maternal care and delivery outcomes.
Area of Science:
- Obstetrics and Gynecology
- Clinical Decision Support Systems
- Maternal Health
Background:
- Cesarean deliveries pose risks, necessitating accurate prediction tools to balance maternal morbidity and unnecessary interventions.
- Existing obstetric calculators aim to predict cesarean delivery risk in diverse clinical scenarios.
Purpose of the Study:
- To review major obstetric calculators for cesarean delivery risk prediction.
- To discuss their clinical applications, accuracy, and limitations.
Main Methods:
- Manual review of PubMed-published research from January 2000 to February 2024.
- Inclusion of articles detailing the creation or validation of delivery mode prediction tools for American populations.
Main Results:
- Developed obstetric calculators show fair accuracy in initial cohorts but often fail to maintain performance in external populations.
- This includes tools for predicting vaginal birth after cesarean and labor induction success.
Conclusions:
- Limited accuracy of cesarean calculators in external settings warrants caution for implementation without refinement.
- Overestimation of cesarean risk can lead to discouraged vaginal deliveries and adverse clinical outcomes.
Related Concept Videos
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
106
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
106
01:52Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
01:12Hazard Ratio
71
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
For example, in a clinical trial...
71
01:25Statistical Methods for Analyzing Epidemiological Data
239
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
239